Recognition of Cyber Physical Systems Through Network Security for Wireless Sensor Networks: Using Artificial InItelligence in Cyber Physical Systems

Introduction

Cyber-Physical Systems (CPS) represent an intricate fusion of computational and physical components that play a pivotal role across diverse sectors, including but not limited to transportation, healthcare, and industrial automation (Santhosh Kumar et al. 2022). Within the architecture of CPS, Wireless Sensor Networks (WSNs) serve as a fundamental element, facilitating the observation and manipulation of physical processes (Josphineleela et al. 2023a). The amalgamation of WSNs into CPS frameworks, however, introduces a series of security vulnerabilities, chiefly due to the inherent nature of wireless communication and the resource constraints of sensor nodes (Reddy et al. 2023). These vulnerabilities expose the systems to a variety of cyber-attacks. To counteract these security threats, Artificial Intelligence (AI) offers a promising solution by delivering sophisticated mechanisms for the detection and neutralization of potential cyber intrusions (Asha et al. 2022). In our study, we put forth a novel strategy that leverages AI for bolstering the security of CPS, specifically through enhanced safeguarding of WSNs. By integrating machine learning algorithms with established network security protocols, our methodology aims to elevate the efficiency of detecting and countering cyber threats within CPS environments (Suman et al. 2023). This approach not only heightens security measures but also adapts to the evolving landscape of cyber challenges faced by CPS (Darshan et al. 2022).

Recent studies have shown that AI can improve the security of CPS by providing advanced methods for detecting and responding to cyber threats (Loganathan et al. 2023). For example, machine learning algorithms have been used to detect malicious traffic in WSNs by analyzing the behavior of nodes and identifying abnormal patterns (Selvi et al. 2023).

Another approach to enhancing the security of CPS is to use game theory, which allows for the modeling of strategic interactions between attackers and defenders. Game-theoretic techniques have been applied to CPS to study the optimal strategies for defending against cyber attacks, and to design robust and secure control systems (Sendrayaperumal et al. 2021). In addition to AI and game theory, traditional network security methods such as encryption and authentication can also be used to enhance the security of CPS (Subramanian et al. 2022). For example, the use of encryption can protect the confidentiality of communication in WSNs, while authentication can prevent unauthorized access to the network (Kaushal et al. 2023).

Overall, the literature suggests that a combination of AI and traditional network security methods can improve the recognition and response to cyber threats in CPS. Our proposed method aims to build on this by developing a new approach for recognizing CPS through network security for WSNs using AI (Thakre et al. 2023). However, it's worth mentioning that the above is a very broad and generic overview, it would be better to focus on the specific aspect of the proposed method, the problem that it attempts to solve, and its novelty with respect to the existing literature (Nagarajan et al. 2022).

In particular, our proposed method aims to address the problem of accurately recognizing cyber threats in CPS, particularly in WSNs. One of the main challenges in this area is the dynamic and unpredictable nature of cyber attacks, which can evade traditional security methods that rely on predefined rules or signatures (Seeniappan et al. 2023). To address this challenge, our method utilizes machine learning techniques to learn from the behavior of the network and identify abnormal patterns that may indicate a cyber attack. Our method also incorporates traditional network security methods such as encryption and authentication to provide an additional layer of protection (Arockia Dhanraj et al. 2022). One of the key novelties of our proposed method is the use of AI techniques to learn the normal behavior of the network, and then use this knowledge to detect and respond to cyber threats (Sharma et al. 2022). This is different from traditional methods that rely on predefined rules or signatures, which can be easily evaded by attackers (Divya et al. 2022).